In this article, we propose a novel deep reinforcement learning (DRL) approach for controlling multiple unmanned aerial vehicles (UAVs) with the ultimate purpose of tracking first responders (FRs) in challenging 3-D environments presence obstacles and occlusions. We assume that UAVs receive noisy distance measurements from FRs which are two types, i.e., Line Sight (LoS) non-LoS (NLoS) used by U...

It is still a challenging problem to develop robust target tracking algorithm under various environments. Most of current target tracking algorithms are able to track objects well in controlled environments, but they usually fail in significant variation of the target’s scale, pose and plane rotation. One reason for such failure is that these object tracking algorithms employ fixed-size trackin...

Recently, Minimum Cost Multicut Formulations have been proposed and proven to be successful in both motion trajectory segmentation and multi-target tracking scenarios. Both tasks benefit from decomposing a graphical model into an optimal number of connected components based on attractive and repulsive pairwise terms. The two tasks are formulated on different levels of granularity and, according...

It has been shown previously that the Histogram Probabilistic Multi-Hypothesis Tracking (HPMHT) algorithm enables tracking of targets distributed across several sensor cells. Two related aspects of the H-PMHT algorithm are discussed in this paper, namely the estimation of target spread, and the use of the negative multinomial distribution to compensate for unobserved sensor cells. The examples ...

The fundamental problem of target tracking is to estimate the state of one or more objects that persist over time, based on noisy measurements contained in a vast quantity of mostly spurious measurement data. Target tracking is closely related to a number of basic problems in statistical modelling and information extraction from noisy data. Multi-stage processing provides a wealth of processing...